Estimating and Visualizing Drivers' Emotions Using the Internet of Digital Reality

Recently, the development of self-driving technology has progressed rapidly. However, self-driving cars have not yet become widespread. Thus, with an aging population, accidents such as road rage and acceleration and brake accidents are likely to continue. Stress is one key reason for such dangerous driving. Thus, technologies must be developed to provide mental support to drivers as required. In this study, we considered estimating driver emotions as a first step along these lines. To this end, we developed a technology to estimate emotions by collecting data on biological signals such as brain waves, heart rate, body movement, and data on a driver's operating status while they are driving. In addition, we introduce a Positive and Negative Affect Schedule (PANAS) to express the psychological states experienced by drivers. We further present the results of an analysis of data on a driver's emotions from PANAS and data obtained from electroencephalogram (EEG) readings and other biological signals from a car. In addition, the relationship between this experimental environment and the Internet of Digital Reality (loll) is described.